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Dynatrace Adds Business KPI Anomaly Detection and Analysis

Dynatrace announced new enhancements to its Digital Business Analytics module. The Dynatrace explainable AI engine, Davis, has been expanded to process business KPIs, such as revenue trends, customer conversions and churn.

In addition, Dynatrace now enables one-click integration with the most popular web analytics solutions including Adobe Analytics. These enhancements facilitate automatic detection of the root cause of business-impacting anomalies and provide organizations with precise answers and insights so they can deliver outstanding digital experiences and better business outcomes.

“Observability data contains a goldmine of insights about business issues that, until the release of our Digital Business Analytics module last October, remained locked inside of IT systems,” said Steve Tack, SVP of Product Management at Dynatrace. “Today, the digital business analytics module gets even better. The addition of business KPIs to our AI engine, Davis, not only unlocks critical data, it also provides organizations with the precise insights and answers required to take action before the business is negatively impacted.”

“Dynatrace Digital Business Analytics gives us additional visibility into user behavior so we can address problems before they impact customer experience,” said Jim Hinze, Director of Software Engineering at Exeter Finance. “From the very first day of use, we were able to determine that the precise root cause of a recent drop in scheduled payments was due to a website performance error. Immediate business results like this get folks really excited about what we’re doing and improve the way our business and IT teams collaborate.”

New enhancements to the Dynatrace Digital Business Analytics module include:

- Enhanced AI with business KPIs – The Dynatrace explainable AI engine, Davis, now processes business data, such as revenue trends, customer conversions and churn, from an expanded set of data sources including logs, services and custom metrics. This delivers even more precise, AI-powered answers, allowing teams to go beyond simply what is happening to understand precisely why it’s happening.

- Real-time business anomaly detection – Dynatrace delivers automatic, real-time alerts on business-impacting anomalies using critical business metrics, including revenue, feature adoption, conversion steps and segmentation characteristics such as customer loyalty status, geolocation and user satisfaction data. As a result, teams can proactively identify and resolve problems before customers are impacted.

- Automatic web analytics integrations – Dynatrace now enables one-click integration with the most popular web analytics tools, including Adobe Analytics and other services. This enables organizations to use segmentation criteria from these web analytics tools to create similar views in Dynatrace. This shared business context makes it easier for teams to optimize customer experience as well as application functionality, helping to ensure proactive problem resolution and better business outcomes.

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Dynatrace Adds Business KPI Anomaly Detection and Analysis

Dynatrace announced new enhancements to its Digital Business Analytics module. The Dynatrace explainable AI engine, Davis, has been expanded to process business KPIs, such as revenue trends, customer conversions and churn.

In addition, Dynatrace now enables one-click integration with the most popular web analytics solutions including Adobe Analytics. These enhancements facilitate automatic detection of the root cause of business-impacting anomalies and provide organizations with precise answers and insights so they can deliver outstanding digital experiences and better business outcomes.

“Observability data contains a goldmine of insights about business issues that, until the release of our Digital Business Analytics module last October, remained locked inside of IT systems,” said Steve Tack, SVP of Product Management at Dynatrace. “Today, the digital business analytics module gets even better. The addition of business KPIs to our AI engine, Davis, not only unlocks critical data, it also provides organizations with the precise insights and answers required to take action before the business is negatively impacted.”

“Dynatrace Digital Business Analytics gives us additional visibility into user behavior so we can address problems before they impact customer experience,” said Jim Hinze, Director of Software Engineering at Exeter Finance. “From the very first day of use, we were able to determine that the precise root cause of a recent drop in scheduled payments was due to a website performance error. Immediate business results like this get folks really excited about what we’re doing and improve the way our business and IT teams collaborate.”

New enhancements to the Dynatrace Digital Business Analytics module include:

- Enhanced AI with business KPIs – The Dynatrace explainable AI engine, Davis, now processes business data, such as revenue trends, customer conversions and churn, from an expanded set of data sources including logs, services and custom metrics. This delivers even more precise, AI-powered answers, allowing teams to go beyond simply what is happening to understand precisely why it’s happening.

- Real-time business anomaly detection – Dynatrace delivers automatic, real-time alerts on business-impacting anomalies using critical business metrics, including revenue, feature adoption, conversion steps and segmentation characteristics such as customer loyalty status, geolocation and user satisfaction data. As a result, teams can proactively identify and resolve problems before customers are impacted.

- Automatic web analytics integrations – Dynatrace now enables one-click integration with the most popular web analytics tools, including Adobe Analytics and other services. This enables organizations to use segmentation criteria from these web analytics tools to create similar views in Dynatrace. This shared business context makes it easier for teams to optimize customer experience as well as application functionality, helping to ensure proactive problem resolution and better business outcomes.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...